Sub-band processing complexity reduction

ABSTRACT

A sub-band processing system that reduces computational complexity and memory requirements includes a processor and a local or distributed memory. Logic stored in the memory partitions a frequency spectrum of bins into a smaller number of sub-bands. The logic enables a lossy compression by designating a magnitude and a designated or derived phase of each bin in the frequency spectrum as representative. The logic renders a lossless compression by decompressing the lossy compressed data and providing lost data based on original spectral relationships contained within the frequency spectrum.

PRIORITY CLAIM

This application claims the benefit of priority from U.S. ProvisionalApplication No. 61/148,661, filed Jan. 30, 2009, which is incorporatedby reference.

BACKGROUND OF THE INVENTION

1. Technical Field

This disclosure relates to sub-band processing, and more particularly tosystems that reduce computational complexity and memory requirements.

2. Related Art

Wideband networks receive and transmit data through radio frequencysignals through inbound and outbound transmissions. The networks maytransmit data, voice, and video simultaneously through multiple channelsthat may be distinguished in frequency. Some wideband networks arecapable of high speed operations and may have a considerably higherthroughput than some narrowband networks. The increased bandwidth ofthese networks may increase the processing loads and memory requirementsof other applications.

Frequency domain based adaptive filtering, for example, may becomputationally intensive because it translates a time domain signalinto multiple frequency components that are separately processed.Translating a time domain signal into multiple frequency componentsincreases the computational complexity and memory usage of some systemswhen a signal's bandwidth increases. As the number of frequencycomponents increase with bandwidth, the computational load and therequired memory increase.

SUMMARY

A sub-band processing system that reduces computational complexity andmemory requirements includes a processor and a local or a distributedmemory. Logic stored in the memory partitions a frequency spectrum ofbins into sub-bands. The logic enables a lossy compression bydesignating a magnitude and a designated or derived phase of each bin inthe frequency spectrum as representative. The logic renders a losslesscompression by decompressing the lossy compressed data and providinglost data based on original spectral relationships contained within thefrequency spectrum.

Other systems, methods, features and advantages will be, or will become,apparent to one with skill in the art upon examination of the followingfigures and detailed description. It is intended that all suchadditional systems, methods, features and advantages be included withinthis description, be within the scope of the invention, and be protectedby the following claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The system may be better understood with reference to the followingdrawings and description. The components in the figures are notnecessarily to scale, emphasis instead being placed upon illustratingthe principles of the invention. Moreover, in the figures, likereferenced numerals designate corresponding parts throughout thedifferent views.

FIG. 1 is a non-overlapping frequency compression of an uncompressedframe.

FIG. 2 is a band-like overlapping frequency compression of anuncompressed frame.

FIG. 3 is non-overlapping compression showing a phase selection.

FIG. 4 is an uncompressed spectrum.

FIG. 5 is an exemplary rotation of bin 5 to the phase of bin 4.

FIG. 6 is an exemplary illustration of band 3.

FIG. 7 is an exemplary illustration of a processed band 3

FIG. 8 is an exemplary restoration of bins from the exemplary processedband 3.

FIG. 9 is an exemplary sub-band processing system.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Due to improvements in transmission rates and device resolutions,networks are providing multi-media, multi-point, and multipletransmission rates for a variety of services. To reduce computationalloads and memory requirements, a sub-band processing system processesdata such that, after it is compressed and decompressed it is restoredto its original format. The system may compress video, sound, text,code, and/or numeric data such that little or no data is lost after abin or file is decompressed. While the data may contain more informationthan may be heard or seen (e.g., perceived by a user), some systemspreserve the original data (or a representative data set) whilecompressing and decompressing operating data through a lossycompression. After further (optional) processing (by an ancillary deviceor system) the sub-band processing system reconstructs and restores thedata. The restored data may maintain the relative magnitude and phase ofthe original data. The restored data may match the originalrelationships (e.g., relative magnitudes and phases)frequency-for-frequency.

The sub-band processing system analysis may occur on frequency domaincharacteristics. To derive frequency domain properties, the signal maybe broken into intervals though a multiplier function (retained in alocal or a distributed computer readable medium) or multiplier devicethat multiplies the signal by a “window” function or a “frame” of fixedduration. To minimize spectral distortion, smooth window functions (suchas Hann, Hamming, etc. retained in the local or the distributed computerreadable medium) or a window filter may be used for the short-timespectral analysis. A time-to-frequency transform device, a DiscreteFourier Transform (DFT) device, or a Fast Fourier Transform (FFT) devicemay transform (or decompose) the short-time based signals into a complexspectrum. The spectrum may be separated into bins of magnitude and phasedata or substantially equivalent complex (e.g. real and imaginary) data.A sub-band (or band) may be represented by a single bin of magnitude andphase spectra, or a collection of consecutive or successive binsrepresented by a common or single magnitude and phase spectra. Table 1shows representative characteristics of an exemplary FFT device.

TABLE 1 Parameters Sample rate 8 11.025 16 22.05 32 44.1 (kHz) FFTlength (N) 256 256 512 512 1024 1024 Number of 129 129 257 257 513 513useful output bins (R) Hz/bin 31.25 43.07 31.25 43.07 31.25 43.07At a sample rate of about 8 kHz, an FFT device may transform the timedomain signal into about 256 bins. Due to the complex symmetry, the FFTdevice may yield about 129 useful bins (e.g., 256/2+1). Each bin mayrepresent a frequency resolution of about 31.25 Hz (e.g., 8 kHz/256).The frequency resolution of other sample rates (e.g., 16 kHz and 32 kHz)may be maintained by changing the FFT length. For example, at 16 kHz,the FFT length may be about double the FFT length of the 8 kHz samplerate. At 32 kHz, the FFT length may be about double the FFT length ofthe 16 kHz sample rate.

In some systems, the magnitude and phase spectra may be obtained fromone or more signal processors that execute a Discrete Fourier Transform(DFT) stored in a local or a distributed memory. The output of the DFTmay be represented by X(k).

$\begin{matrix}{{X(k)} = {\sum\limits_{n = 0}^{N - 1}{{x(n)}^{j\frac{2\pi \; {nk}}{N}}}}} & (1)\end{matrix}$

for k=0 . . . N−1, wherek is the frequency index for each binn is the time index for each sampleN is the length of the DFT (or FFT)

The bins (R) of the FFT (or DFT) device may partitioned into a fewer (orsmaller R>=M) number of sub-bands (M). In some applications, thesub-band processing system may reduce M to a lowest possible integerthat does not affect the performance or quality of a later process. Inthese applications, the system may generate a number of sub-bands thatminimize perceptual error. The applications may exploit the sensitivityof the human auditory system or other systems that do not detect orprocess certain frequencies or are affected by certain signaldistortions.

A lossy compression may compress the data such that some data is lostwhen the data is compressed into the sub-bands. Some sub-band processingsystems compress 2^(q) bins (q is an integer) into individual sub-bands.Other systems apply a perceptual scale (through a processor orcontroller, for example) where the bins are grouped into sub-bands thatmatch the frequency selectivity of the human auditory system. Thesub-bands may comprise non-overlapping or overlapping frequency regionsthat account for a selected or critical band (e.g., a frequencybandwidth that may model an auditory filter) or apply a perceptual scalelike a single or multiple stage rectangular-like bandwidth filter orfilter bank, logarithmic spacing filter or filter bank, Bark filter orfilter bank, Mel or Mel-like filters or filter bank. FIGS. 1 and 2,respectively, describe exemplary non-overlapping and band-likeoverlapping compressions. In each figure the uncompressed bins are shownabove the corresponding compressed sub-bands. The compressions divide avariable sequence of uncompressed bins into a substantially equalsequence of compressed sub-bands. A substantially equal gain or avariable gain may be applied to render compressed sub-bands that aresubstantially flat across the frequency spectrum. Perceptual distortionsmay be minimized by applying lower compression ratios at lowerfrequencies while applying higher compression ratios at higherfrequencies.

Approximate freq Compression Output sub- range (kHz) Input bin numbersratio bands #s 0-1  0 . . . 31 1:1  0 . . . 31 1-2  32 . . . 63 2:1 32 .. . 47 2-4  64 . . . 127 4:1 48 . . . 63 4-Nyquist 128 . . . M 8:1 64 .. . xxOther systems may apply a more perceptually based scheme that partitionsthe frequency spectrum into non-overlapping regions. In thisalternative, the compression may be based on an auditory filterestimate. Each sub-band may be approximately equal to a firstpredetermined frequency band such as ½ ERB (Equivalent RectangularBandwidth) for frequencies below about 4 kHz, and a second predeterminedfrequency band such as 1 ERB for frequencies above about 4 kHz. Moreaggressive compression schemes may be applied when the level ofdistortion or artifacts do not affect (or have little affect on) theperformance of other systems.

Some systems, such as a system that may divide fifteen bins of thespectrum into five sub-bands (e.g., as shown in FIG. 3) may groupsub-bands such that each sub-band is about 0.4 ERB (at a lowcompression) to about 0.875 (at a high compression) ERB. When there isless processor execution speed the sub-bands may be increased. If thereis a need to reduce a processors speed by a millions of instructions persecond (MIPS), for example, some systems increase the sub-bands tolarger ERB values (e.g., each sub-band may be about 1.25 ERB)

While many lossy compression schemes may be used, the sub-bandprocessing system may select or designate a representative phase foreach sub-band. Some sub-band processing system “preserve” or select thephase of a bin within the sub-band that has the lowest frequency (asshown in FIG. 3) within that sub-band. Other systems may select binsnear or at the center of the sub-band, and others may select a phasebased on other structural, functional, or qualitative measures. Analternative sub-band processing system may derive phase through anaverage or weighted average (e.g., an averaging filter, a programmabledynamic weighting filter, a perceptual weighting filter, etc.). Anaverage may comprise a logical operation stored in a local or remotecentral or distributed memory such as an arithmetic mean of the phaseswithin each sub-band. The weights of a weighted average may be based onthe phase correlations common to one or all of the bins that compriseone or more sub-bands.

The selected magnitudes, an average magnitude (e.g., an average of binsthat makeup a band), peaks in the magnitude spectrum, or a function oralgorithm that selects or synthesizes a magnitude of each sub-band maybe designated as representative. When a maximum magnitude system is usedand a maximum magnitude is detected, the bin containing that magnitudeis indexed, stored in memory, and the magnitude is rotated or shifted(e.g., through a phase shifter) to attain the selected or designatedphase. A resulting sub-band value may be transformed to a maximummagnitude selected from its constituent bins and the phase of the“preserved” or selected bin (through a rotation through or shift by aphase differential, e.g., beta_(sub-band1), beta_(sub-band2), etc.). Inthe sub-band processing system, the magnitude, |SBX(m)|, and phase,arg(SBX(m)), for each sub-band may be:

SBX(m)=|SBX(m)|, arg(SBX(m))  (2)

where

|SBX(m)|=max(|X(j _(m))|, |X(j _(m)+1)|, . . . , |X(j _(m) +D_(m)−1)  (3)

arg(SBX(m))=arg(X(j _(m)))  (4)

h _(m)=arg max(|X(j _(m))|, |X(j _(m)+1)|, . . . , |X(j _(m) +D_(m)−1)|)  (5)

for m=0 . . . M−1 and

-   m is the index for each sub-band-   j_(m) is the starting (uncompressed frequency bin) index for    sub-band m, and also the index of the bin whose phase is preserved    for sub-band in-   D_(m) is the number of uncompressed bins that are “compressed” into    sub-band m-   h_(m) is the uncompressed frequency index of the bin that has the    maximum magnitude for sub-band m    In some systems, common bins may be selected from the divided    spectrum to preserve the phase of the sub-bands relative to each    processed frame. In these systems j_(m), and D_(m) may be constant    (e.g., temporally invariant) while h_(m) may change (e.g., time    variant) from one aural or sound frame (or video, sound, text, code,    and/or numeric data) to the next. Such systems may preserve the    phase of the same bin within a sub-band on a frame-by-frame basis    such as for example, always the first bin of a sub-band in each    frame or a common bin of a sub-band in each frame.

FIG. 4 is an uncompressed spectrum of complex vectors representing bins4, 5, 6 and 7 that comprise an exemplary sub-band 3. In sub-band 3, bin5 has the largest to magnitude and is therefore designated asrepresentative (e.g., through a peak magnitude detector). Through apre-selection or a derivation through a device such as a phase detector,the phase of bin 4 is the designated phase. To preserve that phase, thevector representing bin 5 is rotated counter-clockwise or otherwiseadjusted to substantially match the phase of bin 4 while maintaining itsoriginal maximum magnitude (as shown in FIG. 5). The rotated or adjustedversion of bin 5 represents sub-band 3, which effectively attenuates theremaining spectrum within the sub-band (e.g., effectively setting theremaining spectrum to substantially to zero) as shown in FIG. 6. Themagnitudes and phases of the sparse spectrum (e.g., the adjustedsub-band spectrum) may be further processed before the spectrum isreconstructed.

By maintaining magnitude and phase spectra through the sparse spectrum,the spectrum may be further processed in the frequency domain (or otherdomains). Adaptive filtering techniques or devices used by an acousticecho canceller, noise cancellation, or a beam-former, for example, mayprocess a consistent phase that does not change abruptly from frame toframe. Abrupt phase changes that may be a characteristic of othersystems may be identified as an impulse response that causes an acousticecho canceller to diverge. When divergence occurs, a sub-optimal,reduced, or no echo cancellation may occur due to the mismatch betweenthe filter coefficients and the echo path characteristics. When adivergence is declared, an adaptive filter may require time to achieve aconvergence.

When reconstructing the processed spectrum, the original spectral data(or a representative data set or a data set of relative measures) isprocessed so that little or no data is lost when the decompression iscomplete. By processing the original spectral data (or therepresentative data or relative measure data set), the sub-bandprocessing system may achieve a lossless or nearly lossless compression.Some systems may preserve almost the entire original spectrum to avoidgenerating perceivable artifacts when the spectrum is reconstructed.

An overlap-add synthesis may partially reconstruct the spectrum from theprocessed sparse spectrum. An overlap-add synthesis may avoiddiscontinuities in the reconstructed spectrum. For each sub-band, thesystem rotates the processed sub-band to its original relative phase (ora substantially original relative phase), which is relative to thepreserved bin (e.g., through a counter rotation through the phasedifferential, e.g., beta_(sub-band1), beta_(sub-band2), etc.). Forexample, if a bin containing the largest magnitude was rotated betadegrees in one direction, then the system rotates the processed sub-bandby beta degrees in the opposite direction to restore the peak magnitudebin. With the bin restored, the remaining bins that made up the sub-bandare reconstructed by maintaining relative magnitudes and phases of theoriginal spectrum (or representative data or relative measure data set).The magnitude and phase of the remaining reconstructed bins maintain thesame relative magnitude and phase relationship with the restored peakmagnitude bin, as the original spectral bins had with the original peakmagnitude bin. In some alternative systems, frequency-criteria mayaffect phase reconstruction. In one exemplary system, sub-bands thatexceed a predetermined value (e.g., over about 4 kHz), may not maintainrelative phase relationships.

Because further processing (e.g., echo cancellation, noise reduction,beam former, signal attenuators, amplifiers, signal modifier, etc.) mayalter the magnitude and phase of each sub-band, quantitatively eachSBX(m) has been transformed into SBY(m). Equations 6-10 describe how themagnitude and phase for each sub-band may be expanded to its constituentbins. Equation (7) establishes that the magnitude of the restored peakmagnitude bin is equal (or may be substantially equal) to the magnitudeof the processed sub-band. Equation (8) establishes that the phase ofthe restored peak magnitude bin maintains substantially the samerelative phase relationship measured during the partitioning process.Equations (9) and (10), respectively, establish how the remaining binsmay be reconstructed. Once the complex spectrum is restored,

$\begin{matrix}{{{{SBY}(m)} = {{{SBY}(m)}}},{\arg \left( {{SBY}(m)} \right)}} & (6) \\{{{Y\left( h_{m} \right)}} = {{{SBY}(m)}}} & (7) \\{{\arg \left( {Y\left( h_{m} \right)} \right)} = {{\arg \left( {{SBY}(m)} \right)} - {\arg \left( {X\left( j_{m} \right)} \right)} + {\arg \left( {X\left( h_{m} \right)} \right)}}} & (8) \\{{{Y(p)}} = {{{Y\left( h_{m} \right)}} \cdot \frac{{X(p)}}{{X\left( h_{m} \right)}}}} & (9) \\{{\arg \left( {Y(p)} \right)} = {{\arg \left( {Y\left( h_{m} \right)} \right)} - {\arg \left( {X\left( h_{m} \right)} \right)} + {\arg \left( {X(p)} \right)}}} & (10)\end{matrix}$

for m=0 . . . M−1 and

-   m is the index for each sub-band-   j_(m) is the starting (uncompressed frequency bin) index for    sub-band in, and also the index of the bin whose phase is preserved    for sub-band m-   D_(m) is the number of uncompressed bins that are “compressed” into    sub-band m-   h_(m) is the uncompressed frequency index of the bin that has the    maximum magnitude for sub-band m-   p are the indexes in the range [j_(m), j_(m)+D_(m−)1] that do not    equal h_(m)    a time domain signal may be generated by an Inverse Fourier    Transform device (or function stored in a local or a distributed    memory). If windows were used during system analysis, an overlap-add    function may be used for synthesis.

Assuming original sub-band 3 (of FIG. 6) contained echo and wasprocessed to eliminate or minimize the unwanted or undesired additions(echo), processed sub-band 3 may be somewhat attenuated and rotated asshown in FIG. 7. For example, since bin 5 was designated asrepresentative, it may be restored by rotating sub-band 3 clockwise bybeta degrees to maintain the original relative phase to bin 4. Therestored bin 5 maintains a new attenuated (or adjusted) magnitude. Theremaining bins are then scaled and rotated to maintain their originalrelative phase and magnitude relationships to the restored bin as shownin FIG. 8.

Until the spectrum is restored, the original spectrum (or therepresentative data set) may be retained in a computer readable mediumor memory so that the original relative magnitude and phaserelationships may be maintained or restored in the decompressedspectrum. This retention potentially reduces audible artifacts that maybe introduced by a compression scheme.

The system, methods, and descriptions described may be programmed in oneor more controllers, devices, processors (e.g., signal processors). Theprocessors may comprise one or more central processing units thatsupervise the sequence of micro-operations that execute the instructioncode and data coming from memory (e.g., computer readable medium) thatgenerate, support, and/or complete an operation, compression, or signalmodifications. The dedicated applications may support and define thefunctions of the special purpose processor or general purpose processorthat is customized by instruction code (and in some applications may beresident to vehicles). In some systems, a front-end processor mayperform the complementary tasks of gathering data for a processor orprogram to work with, and for making the data and results available toother processors, controllers, or devices.

The systems, methods, and descriptions may program one or more signalprocessors or may be encoded in a signal bearing storage medium, acomputer-readable medium, or may comprise logic 902 stored in a memorythat may be accessible through an interface and is executable by one ormore processors 904 as shown in FIG. 9 (in FIG. 9, N comprises aninteger). Some signal-bearing storage medium or computer-readable mediumcomprise a memory that is unitary or separate (e.g., local or remote)from a device, programmed within a device, such as one or moreintegrated circuits, or retained in memory and/or processed by acontroller or a computer. If the descriptions or methods are performedby software, the software or logic may reside in a memory resident to orinterfaced to one or more processors, devices, or controllers that maysupport a tangible or visual communication interface (e.g., to adisplay), wireless communication interface, or a wireless system.

The memory may retain an ordered listing of executable instructions in aprocessor, device, or controller accessible medium for implementinglogical functions. A logical function may be implemented through digitalcircuitry, through source code, or through analog circuitry. Thesoftware may be embodied in any computer-readable medium orsignal-bearing medium, for use by, or in connection with, an instructionexecutable system, apparatus, and device, resident to system that maymaintain persistent or non-persistent connections. Such a system mayinclude a computer system, a processor-based system, or another systemthat includes an input and output interface that may communicate with apublicly accessible or privately accessible distributed network througha wireless or tangible communication bus through a public and/orproprietary protocol.

A “computer-readable storage medium” “machine-readable medium,”“propagated-signal” medium, and/or “signal-bearing medium” may comprisea medium that stores, communicates, propagates, or transports softwareor data for use by or in connection with an instruction executablesystem, apparatus, or device. The machine-readable medium mayselectively be, but not limited to, an electronic, magnetic, optical,electromagnetic, infrared, or semiconductor system, apparatus, device,or propagation medium. A non-exhaustive list of examples of amachine-readable medium would include: an electrical connection havingone or more wires, a portable magnetic or optical disk, a volatilememory, such as a Random Access Memory (RAM), a Read-Only Memory (ROM),an Erasable Programmable Read-Only Memory (EPROM or Flash memory), or anoptical fiber. A machine-readable medium may also include a tangiblemedium, as the software may be electronically stored as an image or inanother format (e.g., through an optical scan), then compiled, and/orinterpreted or otherwise processed. The processed medium may then bestored in a computer and/or machine memory.

While various embodiments of the invention have been described, it willbe apparent to those of ordinary skill in the art that many moreembodiments and implementations are possible within the scope of theinvention. Accordingly, the invention is not to be restricted except inlight of the attached claims and their equivalents.

1. A system comprising: a first logic stored in a computer-readablemedium and executable by a processor that partitions and stores afrequency spectrum of bins of real and imaginary data into a smallernumber of sub-bands; a second logic stored in the computer-readablemedium and executable by the processor that executes a lossy compressionthat compresses a designated magnitude of one bin in each sub-band thatis representative of that sub-band and a designated phase of one bin ineach sub-band that is representative of that sub-band, such thatmagnitude data and phase data of the frequency spectrum is notmaintained by the lossy compression; and a third logic stored in thecomputer-readable medium and executable by the processor that renders alossless compression by decompressing lossy compressed data rendered bythe second logic and providing magnitude data and phase data notmaintained by the lossy compression based on original spectralrelationships contained within the frequency spectrum stored in thecomputer-readable medium.
 2. The system of claim 1 where the real andimaginary data comprise magnitude and phase spectra.
 3. The system ofclaim 1 where the designated magnitude comprises a designated peakmagnitude.
 4. The system of claim 1 where the designated magnitudecomprises an average magnitude.
 5. The system of claim 1 where thedesignated phase of at least one bin in at least one of the sub-bands isdifferent from an original phase of the at least one bin comprising thedesignated magnitude.
 6. The system of claim 1 where the second logicprocesses a plurality of frames of data and designates a same bin ineach sub-band as a representative phase for each frame of data thesystem processes.
 7. The system of claim 1 where the second logicprocesses a plurality of frames of data and designates a first bin ineach sub-band as a representative phase for each frame of data thesystem processes.
 8. The system of claim 1 where the second logicprocesses a plurality of frames of data and designates a common bin ineach sub-band as a representative phase for each frame of data thesystem processes.
 9. The system of claim 1 where the sub-bands comprisea single bin and a plurality of successive bins of real and imaginarydata.
 10. The system of claim 1 further comprising a multiplier devicethat multiples the frequency spectrum by a window function before thefrequency spectrum is partitioned.
 11. The system of claim 1 furthercomprising a time-to-frequency transform device that decomposes atime-based signal into the frequency spectrum before the frequencyspectrum is partitioned.
 12. The system of claim 1 further comprising aDiscrete Fourier Transform device that decomposes a time-based signalinto the frequency spectrum before the frequency spectrum ispartitioned.
 13. The system of claim 1 further comprising a Fast FourierTransform device that decomposes a time-based signal into the frequencyspectrum before the frequency spectrum is partitioned.
 14. The system ofclaim 1 where the first logic partitions the frequency spectrum of binsof real and imaginary data into sub-bands that match a frequencysensitivity of a human auditory system.
 15. The system of claim 1 wherethe first logic comprises a filter that model a human auditory system.16. The system of claim 1 where the first logic comprises an ERB filter.17. The system of claim 1 where the first logic comprises arectangular-like bandwidth filter.
 18. The system of claim 1 where thefirst logic comprises a Mel filter
 19. The system of claim 1 where thefirst logic applies a plurality of compression ratios based on afrequency range of the bins.
 20. The system of claim 1 where the firstlogic applies a plurality of lossy compression ratios.
 21. The system ofclaim 1 where the sub-bands comprise non-overlapping regions of thefrequency spectrum of bins.
 22. The system of claim 1 where thesub-bands comprise overlapping regions of the frequency spectrum of bins23. The system of claim 1 where the second logic comprises: computerprogram code that indexes each bin containing the designated peakmagnitude in each sub-band; and computer program code that adjusts thephase of each bin containing the designated peak magnitude to thedesignated phase in each sub-band.
 24. The system of claim 23 where theadjustment comprises a vector rotation through a phase differentialstored in the computer readable medium.
 25. The system of claim 1 wherethe computer readable medium comprises a distributed memory.
 26. Thesystem of claim 1 further comprising an acoustic echo canceller thatprocesses the frequency spectrum after the lossy compression and beforethe third logic provides the magnitude data and phase data.
 27. Thesystem of claim 1 further comprising a noise canceller that processesthe frequency spectrum after the lossy compression and before the thirdlogic provides the magnitude data and phase data.
 28. The system ofclaim 1 further comprising a beam former that processes the frequencyspectrum after the lossy compression and before the third logic providesthe magnitude data and phase data.
 29. The system of claim 1 where thethird logic comprises: computer program code that rotates each of thedesignated magnitudes in each sub-band to an original phase position;and computer program code that restores the bins that comprise thesub-bands rendered by the first logic by reconstructing andsubstantially maintaining the relative magnitudes and relative phases ofthe frequency spectrum partitioned by the first logic.
 30. A compressionsystem comprising: a processor that executes a computer readable mediumcomprising: computer program code that partitions a frequency spectrumof bins of real and imaginary data into a smaller number of sub-bandsthrough a first lossy compression; computer program code that executes asecond lossy compression that compresses a designated peak magnitude ofone bin in each sub-band that is representative of that sub-band and adesignated phase of one bin in each sub-band that is representative ofthat sub-band; and computer program code that decompresses lossycompressed data rendered by the second lossy compression andreconstructs magnitude data and phase data not maintained by the secondlossy compression based on original spectral relationships containedwithin the frequency spectrum.
 31. The system of claim 30 where thefirst lossy compression partitions the frequency spectrum of bins ofreal and imaginary data into sub-bands that match a frequencysensitivity of a human auditory system.
 32. The system of claim 30 wherethe first lossy compression applies a plurality of compression ratiosbased on a frequency range of the bins.
 33. The system of claim 30 wherethe second lossy compression comprises: computer program code thatindexes each bin containing the designated peak magnitude in eachsub-band; and computer program code that shifts the phase of each bincontaining the designated peak magnitude to the designated phase in eachsub-band.
 34. The system of claim 30 where the computer program codethat decompresses the lossy compressed data rendered by the second lossycompression: phase adjusts each of the designated magnitudes in eachsub-band to a substantially original phase position; and restores thebins that comprise the sub-bands rendered by the first logic byreconstructing and substantially maintaining the relative magnitudes andrelative phases of the frequency spectrum in each sub-band with respectto phase adjusted designated magnitudes.
 35. A compression systemcomprising: a processor that executes a computer readable mediumcomprising: computer program code that partitions a frequency spectrumof bins of real and imaginary data into a smaller number of sub-bandsthrough a first lossy compression; lossy compression means thatcompresses a designated peak magnitude of one bin in each sub-band and adesignated phase of one bin in each sub-band; and computer program codethat decompresses lossy compressed data rendered by the lossycompression means and reconstructs magnitude data and phase data notmaintained by the lossy compression means based on original spectralrelationships contained within the frequency spectrum.
 36. A losslesscompression method comprising: partitioning a frequency spectrum of binsof real and imaginary data into a smaller number of sub-bands through afirst lossy compression; compressing a designated peak magnitude of onebin in each sub-band that is representative of that sub-band and adesignated phase of one bin in each sub-band that is representative ofthat sub-band by a second lossy compression; decompressing the lossycompressed data rendered by the second lossy compression; andreconstructing and substantially restoring magnitude data and phase datanot maintained by the second lossy compression based on originalspectral relationships contained within the frequency spectrum so thatrestored magnitude data and restored phase data maintain a relativemagnitude and a relative phase of the frequency spectrum.